D-ISN/Collaborative Research: Early Warning Systems for Emerging Epidemics of Illicit Substances

D-ISN/合作研究：非法物质新出现流行病的早期预警系统

• 批准号: 2240409
• 负责人: Weijun Xie
• 金额: $ 33万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2027-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2240409&HistoricalAwards=false
• 关键词: ISN; Collaborative; Research; Early; Warning

The objective of this Disrupting Operations of Illicit Supply Networks (D-ISN) grant is to develop a data-driven analytical framework to support an Early Warning System (EWS) for emerging illicit substance use crises. The opioid overdose epidemic has evolved in three identified phases, beginning with a rise in presciption opioid abuse, to a rapid increase in heroin overdoses, to synthetic opioids (primarily variants of fentanyl) in combination with heroin, cocaine, and counterfeit pills. Each phase has distinct geospatial and temporal signatures, involving both criminal activity and public health patterns. This project is focused on early identification of new emerging threats, such as the current growing veterinary tranquilizer epidemic, through monitoring and analyzing multimodal data in order to understand underlying causal factors and to develop effective response strategies. This study takes a holistic, multi-disciplinary, system-focused approach to advance the fundamental knowledge of illicit drug use patterns in communities, which can help support effective multi-pronged responses from both the supply and demand sides. The project involves PIs from operations research, criminal justice, and public health policy, in collaboration with several regional agencies tasked with drug trafficking prevention. The project will engage and prepare graduate students to develop new analytical tools to respond to complex societal challenges.This project explores a novel EWS framework with transformative learning and optimization methodologies for identifying and responding to emerging illicit substance threats. The project will collect and build on the use of observational data from a variety of sources to build predictive and prescriptive models. In particular, this project will (1) develop a novel geospatially-aware predictive model to detect emerging threats of illicit drugs and identify high-risk communities by exploiting inherent geospatial connections in the data, (2) learn causal pathways through efficient algorithms to uncover the driving factors of the emerging threats among communities, (3) optimize dynamic intervention strategies that can adapt to emerging data from shifting epidemics, and (4) develop a decision support tool as a proof-of-concept of the proposed EWS framework. The predictive modeling and decision-analytic framework are generalizable to EWS in other application areas. The multidisciplinary team will partner with national and regional drug control programs to demonstrate the practical impact of the proposed data-driven EWS framework.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

非法供应网络（D-ISN）赠款的这种破坏操作的目的是开发一个数据驱动的分析框架，以支持新兴非法物质使用危机的预警系统（EWS）。 阿片类药物过量的流行病已经在三个确定的阶段进化，从预言阿片类药物滥用的增加开始，直到海洛因过量剂量迅速增加，与海洛因，可卡因，可卡因，可卡因和抗腐蚀药的合成阿片类药物（主要是芬太尼的主要变体）相结合。 每个阶段都有不同的地理空间和时间签名，涉及犯罪活动和公共卫生模式。 该项目的重点是通过监测和分析多模式数据以了解基本的因果因素并制定有效的响应策略，从而提高了新出现威胁的早期识别，例如当前不断增长的兽医镇静剂流行。 这项研究采用了整体，多学科的，以系统为中心的方法来促进社区中非法使用药物使用模式的基本知识，这可以帮助支持供应和需求方面的有效多管齐下的反应。 该项目涉及来自运营研究，刑事司法和公共卫生政策的PI，与几个负责预防毒品贩运的地区机构合作。该项目将吸引并准备研究生开发新的分析工具，以应对复杂的社会挑战。该项目探索了一种新颖的EWS框架，并具有变革性学习和优化方法，以识别和应对新兴的非法物质威胁。 该项目将基于从各种来源的观察数据来收集和建立，以构建预测性和规范性模型。特别是，该项目（1）将开发一种新型的地理空间意识的预测模型，以检测非法药物的威胁，并通过利用数据中的固有地理空间联系来识别高风险社区，（（2）通过有效的算法学习因果途径，以有效的算法来揭示与社区中新型策略的策略（3）的策略（3），以确定能够确定的策略（3），3）流行病和（4）开发一个决策支持工具，以证明拟议的EWS框架的概念验证。预测建模和决策分析框架可推广到其他应用领域的EWS。多学科团队将与国家和地区药物控制计划合作，以证明拟议的数据驱动的EWS框架的实际影响。该奖项反映了NSF的法定任务，并认为值得通过基金会的智力优点和更广泛的影响来通过评估来支持。